Lab 11

Author

Liying Deng

Step 1

# Load COVID state-level data from NYT
cv_states <- read.csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")

# Load state population data
state_pops <- read.csv("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv")

# Process population data
state_pops$abb <- state_pops$state
state_pops$state <- state_pops$state_name
state_pops$state_name <- NULL

# Merge the two data frames by the appropriate columns (likely 'state' and 'abb')
cv_states <- merge(cv_states, state_pops, by = "state")

Step 2

dim(cv_states)
[1] 58094     9
head(cv_states)
    state       date fips   cases deaths geo_id population pop_density abb
1 Alabama 2023-01-04    1 1587224  21263      1    4887871    96.50939  AL
2 Alabama 2020-04-25    1    6213    213      1    4887871    96.50939  AL
3 Alabama 2023-02-26    1 1638348  21400      1    4887871    96.50939  AL
4 Alabama 2022-12-03    1 1549285  21129      1    4887871    96.50939  AL
5 Alabama 2020-05-06    1    8691    343      1    4887871    96.50939  AL
6 Alabama 2021-04-21    1  524367  10807      1    4887871    96.50939  AL
tail(cv_states)
        state       date fips  cases deaths geo_id population pop_density abb
58089 Wyoming 2022-09-11   56 175290   1884     56     577737    5.950611  WY
58090 Wyoming 2022-08-21   56 173487   1871     56     577737    5.950611  WY
58091 Wyoming 2021-01-26   56  51152    596     56     577737    5.950611  WY
58092 Wyoming 2021-02-21   56  53795    662     56     577737    5.950611  WY
58093 Wyoming 2021-08-22   56  70671    809     56     577737    5.950611  WY
58094 Wyoming 2021-03-20   56  55581    693     56     577737    5.950611  WY
str(cv_states)
'data.frame':   58094 obs. of  9 variables:
 $ state      : chr  "Alabama" "Alabama" "Alabama" "Alabama" ...
 $ date       : chr  "2023-01-04" "2020-04-25" "2023-02-26" "2022-12-03" ...
 $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
 $ cases      : int  1587224 6213 1638348 1549285 8691 524367 1321892 1088370 1153149 814025 ...
 $ deaths     : int  21263 213 21400 21129 343 10807 19676 16756 16826 15179 ...
 $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
 $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
 $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
 $ abb        : chr  "AL" "AL" "AL" "AL" ...

Step 3

# format the date
cv_states$date <- as.Date(cv_states$date, format="%Y-%m-%d")

# format the state and state abbreviation (abb) variables
state_list <- unique(cv_states$state)
cv_states$state <- factor(cv_states$state, levels = state_list)
abb_list <- unique(cv_states$abb)
cv_states$abb <- factor(cv_states$abb, levels = abb_list)

### FINISH THE CODE HERE 
# order the data first by state, second by date
cv_states <- cv_states[order(cv_states$state, cv_states$date),]

# Confirm the variables are now correctly formatted
str(cv_states)
'data.frame':   58094 obs. of  9 variables:
 $ state      : Factor w/ 52 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ date       : Date, format: "2020-03-13" "2020-03-14" ...
 $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
 $ cases      : int  6 12 23 29 39 51 78 106 131 157 ...
 $ deaths     : int  0 0 0 0 0 0 0 0 0 0 ...
 $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
 $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
 $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
 $ abb        : Factor w/ 52 levels "AL","AK","AZ",..: 1 1 1 1 1 1 1 1 1 1 ...
head(cv_states)
       state       date fips cases deaths geo_id population pop_density abb
1029 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
597  Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
282  Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
12   Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
266  Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
78   Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL
tail(cv_states)
        state       date fips  cases deaths geo_id population pop_density abb
57902 Wyoming 2023-03-18   56 185640   2009     56     577737    5.950611  WY
57916 Wyoming 2023-03-19   56 185640   2009     56     577737    5.950611  WY
57647 Wyoming 2023-03-20   56 185640   2009     56     577737    5.950611  WY
57867 Wyoming 2023-03-21   56 185800   2014     56     577737    5.950611  WY
58057 Wyoming 2023-03-22   56 185800   2014     56     577737    5.950611  WY
57812 Wyoming 2023-03-23   56 185800   2014     56     577737    5.950611  WY
# Inspect the range values for each variable. What is the date range? The range of cases and deaths?
head(cv_states)
       state       date fips cases deaths geo_id population pop_density abb
1029 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
597  Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
282  Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
12   Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
266  Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
78   Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL
summary(cv_states)
           state            date                 fips           cases         
 Washington   : 1158   Min.   :2020-01-21   Min.   : 1.00   Min.   :       1  
 Illinois     : 1155   1st Qu.:2020-12-06   1st Qu.:16.00   1st Qu.:  112125  
 California   : 1154   Median :2021-09-11   Median :29.00   Median :  418120  
 Arizona      : 1153   Mean   :2021-09-10   Mean   :29.78   Mean   :  947941  
 Massachusetts: 1147   3rd Qu.:2022-06-17   3rd Qu.:44.00   3rd Qu.: 1134318  
 Wisconsin    : 1143   Max.   :2023-03-23   Max.   :72.00   Max.   :12169158  
 (Other)      :51184                                                          
     deaths           geo_id        population        pop_density       
 Min.   :     0   Min.   : 1.00   Min.   :  577737   Min.   :    1.292  
 1st Qu.:  1598   1st Qu.:16.00   1st Qu.: 1805832   1st Qu.:   43.659  
 Median :  5901   Median :29.00   Median : 4468402   Median :  107.860  
 Mean   : 12553   Mean   :29.78   Mean   : 6397965   Mean   :  423.031  
 3rd Qu.: 15952   3rd Qu.:44.00   3rd Qu.: 7535591   3rd Qu.:  229.511  
 Max.   :104277   Max.   :72.00   Max.   :39557045   Max.   :11490.120  
                                                     NA's   :1106       
      abb       
 WA     : 1158  
 IL     : 1155  
 CA     : 1154  
 AZ     : 1153  
 MA     : 1147  
 WI     : 1143  
 (Other):51184  
min(cv_states$date)
[1] "2020-01-21"
max(cv_states$date)
[1] "2023-03-23"

Conclusion: The date range min (2020-01-21) to max (2023-03-23)

Step 4

library(dplyr)

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
# Add variables for new_cases and new_deaths:
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])
  cv_subset = cv_subset[order(cv_subset$date),]

  # add starting level for new cases and deaths
  cv_subset$new_cases = cv_subset$cases[1]
  cv_subset$new_deaths = cv_subset$deaths[1]

  ### FINISH THE CODE HERE
  for (j in 2:nrow(cv_subset)) {
    cv_subset$new_cases[j] <- cv_subset$cases[j] - cv_subset$cases[j - 1]
    cv_subset$new_deaths[j] <- cv_subset$deaths[j] - cv_subset$deaths[j - 1]
  }

  # include in main dataset
  cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases
  cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths
}

# Focus on recent dates
cv_states <- cv_states %>%
  filter(date >= as.Date("2021-06-01"))

### FINISH THE CODE HERE
# Inspect outliers in new_cases using plotly

library(ggplot2)
library(plotly)

Attaching package: 'plotly'
The following object is masked from 'package:ggplot2':

    last_plot
The following object is masked from 'package:stats':

    filter
The following object is masked from 'package:graphics':

    layout
p1<-ggplot(cv_states, aes(x = date, y = new_cases, color = state)) + labs(title = "New Cases by Date for Each State") + geom_point(size = .5, alpha = 0.5)
ggplotly(p1)
p1<-NULL # to clear from workspace

p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + labs(title = "New Deaths by Date for Each State") + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)
p2<-NULL # to clear from workspace

# set negative new case or death counts to 0
cv_states$new_cases[cv_states$new_cases<0] = 0
cv_states$new_deaths[cv_states$new_deaths<0] = 0

# Recalculate `cases` and `deaths` as cumulative sum of updated `new_cases` and `new_deaths`
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])

  # add starting level for new cases and deaths
  cv_subset$cases = cv_subset$cases[1]
  cv_subset$deaths = cv_subset$deaths[1]

  ### FINISH CODE HERE
  for (j in 2:nrow(cv_subset)) {
    cv_subset$cases[j] <- cv_subset$new_cases[j] + cv_subset$cases[j - 1]
    cv_subset$deaths[j] <- cv_subset$new_deaths[j] + cv_subset$deaths[j - 1]
  }
  # include in main dataset
  cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases
  cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths
}

library(zoo)

Attaching package: 'zoo'
The following objects are masked from 'package:base':

    as.Date, as.Date.numeric
# Smooth new counts
cv_states$new_cases = zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') %>% round(digits = 0)
cv_states$new_deaths = zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') %>% round(digits = 0)

# Inspect data again interactively
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)
#p2=NULL

Step 5

# Add population normalized (by 100,000) counts for each variable
cv_states$per100k <- as.numeric(format(round(cv_states$cases / (cv_states$population / 100000), 1), nsmall = 1))
cv_states$newper100k <- as.numeric(format(round(cv_states$new_cases / (cv_states$population / 100000), 1), nsmall = 1))
Warning: NAs introduced by coercion
cv_states$deathsper100k <- as.numeric(format(round(cv_states$deaths / (cv_states$population / 100000), 1), nsmall = 1))
cv_states$newdeathsper100k <- as.numeric(format(round(cv_states$new_deaths / (cv_states$population / 100000), 1), nsmall = 1))
Warning: NAs introduced by coercion
# Add a naive CFR variable = deaths / cases
cv_states <- cv_states %>% mutate(naive_CFR = round((deaths * 100 / cases), 2))

# Create a `cv_states_today` variable
cv_states_today <- subset(cv_states, date == max(cv_states$date))

#Step 6

### FINISH CODE HERE
library(plotly)
cv_states <- cv_states %>%
  mutate(
    per100k = as.numeric(ifelse(population > 0 & !is.na(population),
                                round(cases / (population / 100000), 1), NA)),
    deathsper100k = as.numeric(ifelse(population > 0 & !is.na(population),
                                      round(deaths / (population / 100000), 1), NA))
  )
cv_states <- cv_states %>%
  mutate(naive_CFR = as.numeric(ifelse(cases > 0 & !is.na(cases),
                                       round((deaths * 100 / cases), 2), NA)))
cv_states_today <- cv_states %>%
  filter(date == max(date))
print("Columns in cv_states_today:")
[1] "Columns in cv_states_today:"
print(names(cv_states_today))
 [1] "state"            "date"             "fips"             "cases"           
 [5] "deaths"           "geo_id"           "population"       "pop_density"     
 [9] "abb"              "new_cases"        "new_deaths"       "per100k"         
[13] "newper100k"       "deathsper100k"    "newdeathsper100k" "naive_CFR"       
# pop_density vs. cases
cv_states_today %>%
  plot_ly(x = ~pop_density, y = ~cases,
          type = "scatter", mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), 
          marker = list(sizemode = 'diameter'), opacity = 0.5)
Warning: Ignoring 1 observations
Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
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# Filter out "District of Columbia"
cv_states_today_filter <- cv_states_today %>% filter(state != "District of Columbia")

# pop_density vs. cases after filtering
cv_states_today_filter %>%
  plot_ly(x = ~pop_density, y = ~cases,
          type = "scatter", mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), 
          marker = list(sizemode = 'diameter'), opacity = 0.5)
Warning: Ignoring 1 observations
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names(cv_states_today_filter)
 [1] "state"            "date"             "fips"             "cases"           
 [5] "deaths"           "geo_id"           "population"       "pop_density"     
 [9] "abb"              "new_cases"        "new_deaths"       "per100k"         
[13] "newper100k"       "deathsper100k"    "newdeathsper100k" "naive_CFR"       
cv_states_today_filter %>%
  plot_ly(x = ~pop_density, y = ~deathsper100k,
          type = "scatter", mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70),
          marker = list(sizemode = 'diameter'), opacity = 0.5)
Warning: Ignoring 1 observations
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
# Adding hoverinfo
cv_states_today_filter %>%
  plot_ly(x = ~pop_density, y = ~deathsper100k,
          type = "scatter", mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70),
          marker = list(sizemode = 'diameter'), opacity = 0.5,
          hoverinfo = 'text',
          text = ~paste(
            paste(state, ":", sep = ""),
            paste(" Cases per 100k: ", per100k, sep = ""),
            paste(" Deaths per 100k: ", deathsper100k, sep = ""),
            sep = "<br>")
          ) %>%
  layout(
    title = "Population-normalized COVID-19 deaths (per 100k) vs. population density for US states",
    yaxis = list(title = "Deaths per 100k"),
    xaxis = list(title = "Population Density"),
    hovermode = "compare"
  )
Warning: Ignoring 1 observations
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

Step 8

library(dplyr)
# Calculate new_cases and new_deaths
cv_states <- cv_states %>%
  arrange(state, date) %>%  
  group_by(state) %>%
  mutate(
    new_cases = cases - lag(cases),
    new_deaths = deaths - lag(deaths)
  ) %>%
  ungroup()
### FINISH CODE HERE
# Line chart for naive_CFR for all states over time using `plot_ly()`
plot_ly(cv_states, x = ~date, y = ~naive_CFR, color = ~state,
        type = "scatter", mode = "lines") %>%
  layout(
    title = "Naive CFR Over Time for All States",
    xaxis = list(title = "Date"),
    yaxis = list(title = "Naive CFR (%)")
  )
Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
### FINISH CODE HERE
# Line chart for Florida showing new_cases and new_deaths together
cv_states %>%
  filter(state == "Florida") %>%
  plot_ly(x = ~date) %>%
  add_trace(y = ~new_cases, type = "scatter", mode = "lines", name = "New Cases") %>%
  add_trace(y = ~new_deaths, type = "scatter", mode = "lines", name = "New Deaths") %>%
  layout(
    title = "New Cases and New Deaths Over Time in Florida",
    xaxis = list(title = "Date"),
    yaxis = list(title = "Count"),
    legend = list(title = list(text = "Metric"))
  )

Step 9

### FINISH CODE HERE
# Map state, date, and new_cases to a matrix
library(tidyr)
cv_states_mat <- cv_states %>% select(state, date, new_cases) %>% dplyr::filter(date>as.Date("2021-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)

# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)
# Calculate new cases per 100,000 people
cv_states <- cv_states %>%
  mutate(
    newper100k = as.numeric(ifelse(population > 0 & !is.na(population),
                                   round(new_cases / (population / 100000), 1), NA))
  )
# Repeat with newper100k
cv_states_mat <- cv_states %>% select(state, date, newper100k) %>% dplyr::filter(date>as.Date("2021-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)

plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)
# Create a second heatmap after filtering to only include dates every other week
filter_dates <- seq(as.Date("2021-06-15"), as.Date("2021-11-01"), by="2 weeks")

cv_states_mat <- cv_states %>% select(state, date, newper100k) %>% filter(date %in% filter_dates)
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)

# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)

Step 10

### For specified date

pick.date = "2021-10-15"

# Extract the data for each state by its abbreviation
cv_per100 <- cv_states %>% filter(date==pick.date) %>% select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL

# Create hover text
cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))

# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

# Make sure both maps are on the same color scale
shadeLimit <- 125

# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') %>% 
  add_trace(
    z = ~newper100k, text = ~hover, locations = ~state,
    color = ~newper100k, colors = 'Purples'
  )
fig <- fig %>% colorbar(title = paste0("Cases per 100k: ", pick.date), limits = c(0,shadeLimit))
fig <- fig %>% layout(
    title = paste('Cases per 100k by State as of ', pick.date, '<br>(Hover for value)'),
    geo = set_map_details
  )
fig_pick.date <- fig
#############
### Map for today's date
cv_states <- cv_states %>%
  mutate(
    newper100k = as.numeric(ifelse(population > 0 & !is.na(population),
                                   round(new_cases / (population / 100000), 1), NA))
  )
# Filter for the most recent date to create `cv_states_today`
cv_states_today <- cv_states %>%
  filter(date == max(date))

# Extract the data for each state by its abbreviation
cv_per100 <- cv_states_today %>%  select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL

# Create hover text
cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))

# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') %>% 
  add_trace(
    z = ~newper100k, text = ~hover, locations = ~state,
    color = ~newper100k, colors = 'Purples'
  )
fig <- fig %>% colorbar(title = paste0("Cases per 100k: ", Sys.Date()), limits = c(0,shadeLimit))
fig <- fig %>% layout(
    title = paste('Cases per 100k by State as of', Sys.Date(), '<br>(Hover for value)'),
    geo = set_map_details
  )
fig_Today <- fig


### Plot together 
subplot(fig_pick.date, fig_Today, nrows = 2, margin = .05)%>%
  layout(title = "Combined Plot of Selected Date and Today's Data")